{"as_of":"2026-08-21T03:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a97d339c63764a3fefe3d253dabbf3afa8ca668944f3508398f38f3e6b78d7c4","coverage":[{"denominator":25,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":25,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T10:17:02.560022Z","state":"measured"},{"denominator":26,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":26,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T10:17:00.347016Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-05T10:17:02.643735Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","version":1},"cited_work":{"arxiv_id":"2509.04298","doi":null,"metadata_source":"pith","pith_arxiv_id":"2509.04298","snapshot_observed_at":"2026-08-05T10:17:02.643735Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","venue":"cs.CV","work_id":"050ae718-2f89-4b52-b4cb-ded12369101a","year":2025},"citing_paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T10:17:00.347016Z"},"links":{"cited_paper":"/paper/2509.04298","citing_paper":"/paper/2509.04298"},"observation_digest":"sha256:74563a9c944d06935c3a213c234645679fa8a7501bfe66550953bbdb86846aeb","observation_id":"9b54581c-a8dc-4113-bbd4-9caa51d607b1","resolution":{"observed_at":"2026-08-05T10:17:02.647292Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2509.04298/citation-record","integrity":"/paper/2509.04298/integrity","json":"/paper/2509.04298/citation-record.json","paper":"/paper/2509.04298"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","version":1},"cited_work":{"arxiv_id":"2509.04298","doi":null,"metadata_source":"pith","pith_arxiv_id":"2509.04298","snapshot_observed_at":"2026-08-05T10:17:02.643735Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","venue":"cs.CV","work_id":"050ae718-2f89-4b52-b4cb-ded12369101a","year":2025},"citing_paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T10:17:00.347016Z"},"links":{"cited_paper":"/paper/2509.04298","citing_paper":"/paper/2509.04298"},"observation_digest":"sha256:74563a9c944d06935c3a213c234645679fa8a7501bfe66550953bbdb86846aeb","observation_id":"9b54581c-a8dc-4113-bbd4-9caa51d607b1","resolution":{"observed_at":"2026-08-05T10:17:02.647292Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:17:02.821187Z","title":"Noisy label learning Conventional research on noisy label learning [6, 7] was pri- marily based on an i.i.d","venue":null,"work_id":"58d87cf9-f507-4313-a765-4438bbf99885","year":null},"citing_paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T10:17:00.427789Z"},"links":{"citing_paper":"/paper/2509.04298"},"observation_digest":"sha256:5036dc38dd1b37e3f57427cb2f9f63d86565684fdd7104939cbe8fea75466daa","observation_id":"9d787659-acac-49ad-b047-f19e7594e3d3","resolution":{"observed_at":"2026-08-05T10:17:02.824385Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:17:02.812377Z","title":"A photo of c","venue":null,"work_id":"dc966f06-7d04-4f77-92d0-74b4f93d0d2e","year":null},"citing_paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T10:17:00.529370Z"},"links":{"citing_paper":"/paper/2509.04298"},"observation_digest":"sha256:829f581da6930d9109c1ab65e8b7ae0717845991fe2fb4ab8d9991049f5ec0bd","observation_id":"5ffa6916-bc1a-437c-b616-670b8bb1120d","resolution":{"observed_at":"2026-08-05T10:17:02.815236Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:17:02.802910Z","title":"Experimental setup Datasets and noise types","venue":null,"work_id":"f55fea84-bc1d-4e13-94f7-5ceab4e109c0","year":null},"citing_paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T10:17:00.587832Z"},"links":{"citing_paper":"/paper/2509.04298"},"observation_digest":"sha256:877df79c8aa2c241931082a5d99fb4162cfcbc0e667187188018136e819fc10f","observation_id":"25f87d1d-f7b9-4f62-811c-bfd34066c10a","resolution":{"observed_at":"2026-08-05T10:17:02.805801Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:17:02.793781Z","title":null,"venue":null,"work_id":"e438c91c-6450-4264-b71a-cb87b5de7ac2","year":null},"citing_paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T10:17:00.670250Z"},"links":{"citing_paper":"/paper/2509.04298"},"observation_digest":"sha256:00927c295e3f45f3a765b0a656c169c43371c6c5b53f991e3b93e60ed4e83590","observation_id":"51f42e7b-d8ba-4e53-8247-255016aa8452","resolution":{"observed_at":"2026-08-05T10:17:02.796704Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:17:02.784524Z","title":"Learning with feature- dependent label noise: A progressive approach,","venue":null,"work_id":"c1201f0a-fec6-4a90-a556-efbaad44671e","year":2021},"citing_paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T10:17:00.782448Z"},"links":{"citing_paper":"/paper/2509.04298"},"observation_digest":"sha256:4e569400a91b1d0d8c55167cb63de12846200a31bc6f72f5971640fc7de8b229","observation_id":"9022bb07-9034-4aea-9a09-40187a008aaf","resolution":{"observed_at":"2026-08-05T10:17:02.787669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2204.06125","last_updated":"2022-04-13T01:10:33Z","snapshot_observed_at":"2026-08-15T12:50:58.405488Z","submitted_at":"2022-04-13T01:10:33Z","title":"Hierarchical Text-Conditional Image Generation with CLIP Latents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.06125","snapshot_observed_at":"2026-08-05T10:17:00.939302Z","title":"Hierarchical text-conditional image generation with clip latents,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T10:17:00.939302Z"},"links":{"cited_paper":"/paper/2204.06125","citing_paper":"/paper/2509.04298"},"observation_digest":"sha256:3f51c582d1d78655a13a19964ad2dfcde0fcf22ec4239ced8d1c2ac3d9267981","observation_id":"7627d908-e725-410f-8e4b-9a110a7b57e8","resolution":{"observed_at":"2026-08-05T10:17:00.939302Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:17:02.774412Z","title":"High-resolution im- age synthesis with latent diffusion models,","venue":null,"work_id":"0bf30964-b317-45ce-92ee-dd9e5e8fff53","year":2022},"citing_paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T10:17:01.047899Z"},"links":{"citing_paper":"/paper/2509.04298"},"observation_digest":"sha256:9ad1512be47a94bf5e1092e71153c8cd0960f9fc677f38a0e8d93d259c453be2","observation_id":"b1c73cda-f4ee-4976-a753-78dbd25b8a1c","resolution":{"observed_at":"2026-08-05T10:17:02.777878Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:17:02.764083Z","title":"Will large-scale generative models corrupt future datasets?,","venue":null,"work_id":"a1e5cf3e-c97e-4146-b8ba-8daa2a3ea9fc","year":2023},"citing_paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T10:17:01.181357Z"},"links":{"citing_paper":"/paper/2509.04298"},"observation_digest":"sha256:456d11d2511ee9d0af831842ba422c8a3f2e4abdbd94649875d8be1cbb533fc6","observation_id":"7e1a3cc4-5649-4430-8cb6-aa8a897467f3","resolution":{"observed_at":"2026-08-05T10:17:02.767261Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:17:02.754008Z","title":"Fake it till you make it: Learn- ing transferable representations from synthetic imagenet clones,","venue":null,"work_id":"b2be030e-e11b-452f-bf35-d87922e6446c","year":2023},"citing_paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T10:17:01.297519Z"},"links":{"citing_paper":"/paper/2509.04298"},"observation_digest":"sha256:ca536487ff8a417a39f5d5ba603157598aaa899d6384733d68c9fe973bb1caac","observation_id":"a6af25ee-081d-44f5-8ccb-f8948845b2df","resolution":{"observed_at":"2026-08-05T10:17:02.757599Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:17:02.744182Z","title":"Joint optimization framework for learning with noisy labels,","venue":null,"work_id":"4509ccbd-6117-4bbe-80dc-ed2ccf991e51","year":2018},"citing_paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T10:17:01.488537Z"},"links":{"citing_paper":"/paper/2509.04298"},"observation_digest":"sha256:3e92a8953225f2f208e4d1925409b5cd71456620d13b03f72c24a20ecf648c2b","observation_id":"9bcae6a1-5ad8-491e-83a3-92d89ce9890f","resolution":{"observed_at":"2026-08-05T10:17:02.747624Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.10456","last_updated":"2021-12-07T11:42:39Z","snapshot_observed_at":"2026-08-16T17:47:57.202449Z","submitted_at":"2021-10-20T09:39:50Z","title":"Noisy Annotation Refinement for Object Detection","version":2},"cited_work":{"arxiv_id":"2110.10456","doi":null,"metadata_source":"pith","pith_arxiv_id":"2110.10456","snapshot_observed_at":"2026-08-05T10:17:02.617752Z","title":"Noisy Annotation Refinement for Object Detection","venue":"cs.CV","work_id":"613dacca-feab-40cd-884e-b8b407617708","year":2021},"citing_paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T10:17:01.542017Z"},"links":{"cited_paper":"/paper/2110.10456","citing_paper":"/paper/2509.04298"},"observation_digest":"sha256:66ca372b7541f26f75bfce66b9a0ca73056d5e530d73395071e25913df8ab059","observation_id":"28ce3a3a-2f87-4fce-b67a-31d8058a7e55","resolution":{"observed_at":"2026-08-05T10:17:02.623103Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:17:02.734391Z","title":"Label- retrieval-augmented diffusion models for learning from noisy labels,","venue":null,"work_id":"17d53496-3687-4e3a-a1f1-ad1d2da20a16","year":2024},"citing_paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T10:17:01.654170Z"},"links":{"citing_paper":"/paper/2509.04298"},"observation_digest":"sha256:202f50273a71be317f35da247b6fbb419995d55efc695a998d53ff2eba7630e9","observation_id":"fae547af-968e-4714-9edc-f639b03b6173","resolution":{"observed_at":"2026-08-05T10:17:02.738006Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.07574","last_updated":"2023-02-15T06:26:38Z","snapshot_observed_at":"2026-08-20T06:56:32.381613Z","submitted_at":"2022-10-14T06:54:24Z","title":"Is synthetic data from generative models ready for image recognition?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.07574","snapshot_observed_at":"2026-08-05T10:17:01.818069Z","title":"Is synthetic data from generative models ready for image recognition?,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T10:17:01.818069Z"},"links":{"cited_paper":"/paper/2210.07574","citing_paper":"/paper/2509.04298"},"observation_digest":"sha256:b6e424890ec6ca3aaf7eccd1be7a3af2a039b91ff1e45040d648838a40ed48fd","observation_id":"4e5eea64-60b3-417f-91e7-11f4f39f776b","resolution":{"observed_at":"2026-08-05T10:17:01.818069Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:17:02.724052Z","title":"Imagenet large scale visual recognition challenge,","venue":null,"work_id":"083d7675-6dac-41ea-a30b-a0af83318f21","year":2015},"citing_paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T10:17:01.939180Z"},"links":{"citing_paper":"/paper/2509.04298"},"observation_digest":"sha256:9f1bf41048deb02d7e433c2875074a7b660c01661f4937c0c1790efc7aab368e","observation_id":"b8bc3ef9-0b4d-4db1-b1e2-e97cb735cb36","resolution":{"observed_at":"2026-08-05T10:17:02.727648Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:17:02.093559Z","title":"Learning multiple layers of features from tiny images,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T10:17:02.093559Z"},"links":{"citing_paper":"/paper/2509.04298"},"observation_digest":"sha256:082602d8bbe849296a78f063dc547bf9a537a18c774e9d703790ee5ca453ca97","observation_id":"f5586c66-70b0-4fbf-9fc6-1140e5cc7389","resolution":{"observed_at":"2026-08-05T10:17:02.093559Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:17:02.707980Z","title":"Adversarial diffusion distillation,","venue":null,"work_id":"f9b6e1e5-96b4-49f0-a643-fb792b3c8533","year":2025},"citing_paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T10:17:02.116292Z"},"links":{"citing_paper":"/paper/2509.04298"},"observation_digest":"sha256:f817d85b37a9598248f5f66a0aae3c8f8d83774197ad87288357341347773d69","observation_id":"a7be80ab-17c1-47b9-89ea-0b6d4db31a86","resolution":{"observed_at":"2026-08-05T10:17:02.711265Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01952","last_updated":"2023-07-04T23:04:57Z","snapshot_observed_at":"2026-08-14T22:54:08.184266Z","submitted_at":"2023-07-04T23:04:57Z","title":"SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01952","snapshot_observed_at":"2026-08-05T10:17:02.189047Z","title":"Sdxl: Improving latent diffu- sion models for high-resolution image synthesis,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T10:17:02.189047Z"},"links":{"cited_paper":"/paper/2307.01952","citing_paper":"/paper/2509.04298"},"observation_digest":"sha256:547ba46fcb5490656a5b4565cd30008e255f9ed6893ee5b4963bf3cbd1a0e27e","observation_id":"5225f9e3-83bb-4716-b866-175aa88ebb97","resolution":{"observed_at":"2026-08-05T10:17:02.189047Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:17:02.357268Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T10:17:02.357268Z"},"links":{"citing_paper":"/paper/2509.04298"},"observation_digest":"sha256:d3d6dd38e8202f61a06d84afb7f59e8c74f3e739e037563bfe1a7b54b541a0e6","observation_id":"c3b01973-4702-4293-b88e-008b94073b98","resolution":{"observed_at":"2026-08-05T10:17:02.357268Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:17:02.691246Z","title":"Generalized cross en- tropy loss for training deep neural networks with noisy labels,","venue":null,"work_id":"65b7cb70-e1a8-4e14-b4ee-641491684558","year":2018},"citing_paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T10:17:02.439686Z"},"links":{"citing_paper":"/paper/2509.04298"},"observation_digest":"sha256:e73e5774bebe7309598dc6a01fc4b927f7b66694d1646d560e884a0e674890d2","observation_id":"06476c85-8c2e-457d-b104-a2b33ef62ac4","resolution":{"observed_at":"2026-08-05T10:17:02.694873Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:17:02.681960Z","title":"Symmetric cross entropy for robust learning with noisy labels,","venue":null,"work_id":"c7482e39-3dc0-493b-b25d-e3c86a0caf3a","year":2019},"citing_paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T10:17:02.540111Z"},"links":{"citing_paper":"/paper/2509.04298"},"observation_digest":"sha256:f03b9ed0e0489e83d746bf094b10318ba17293752ea5ce9f51f8bd28a5382f32","observation_id":"d1d2709a-ee86-4f01-958b-adc61f9740ac","resolution":{"observed_at":"2026-08-05T10:17:02.684960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:17:02.672726Z","title":"Error-bounded correction of noisy labels,","venue":null,"work_id":"39fa4e42-bcf8-4365-92e3-90ed08fad277","year":2020},"citing_paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T10:17:02.550962Z"},"links":{"citing_paper":"/paper/2509.04298"},"observation_digest":"sha256:0ad1ad6fe7704e29af6a451cce129f5e8ad1c7f7af886e7b37084541afa415f8","observation_id":"43b783b0-60d6-475b-907e-43b44215d051","resolution":{"observed_at":"2026-08-05T10:17:02.675818Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:17:02.663316Z","title":"Centrality and consistency: two-stage clean samples identification for learning with instance- dependent noisy labels,","venue":null,"work_id":"0ea97f64-fe66-40ea-8610-999ac7ea329d","year":2022},"citing_paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T10:17:02.554005Z"},"links":{"citing_paper":"/paper/2509.04298"},"observation_digest":"sha256:4836c368c0be6e5f3d6ddf2ca718cf0994ad43936738d283e5b247812f98a815","observation_id":"d5eeee2f-6f05-4421-b793-d528df4cd74d","resolution":{"observed_at":"2026-08-05T10:17:02.666647Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:17:02.653867Z","title":"Learning transferable visual models from natural lan- guage supervision,","venue":null,"work_id":"78ab984b-ba8f-4d3b-9c57-fc3b3137b8b3","year":2021},"citing_paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T10:17:02.556933Z"},"links":{"citing_paper":"/paper/2509.04298"},"observation_digest":"sha256:cf7ea6dec68d03f589eca63c561d2a069bc61630c49b298d5c86669dc6239447","observation_id":"289080ec-a5d5-4019-94cc-ab82c419626b","resolution":{"observed_at":"2026-08-05T10:17:02.657115Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-16T09:25:53.087782Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-05T10:17:02.560022Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T10:17:02.560022Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2509.04298"},"observation_digest":"sha256:613027a7118892d4cae97999ef17e328f56e7eef9be5b7ee41b4c833b5fb2437","observation_id":"deffef04-547d-41ad-bfa3-8afc8fab1168","resolution":{"observed_at":"2026-08-05T10:17:02.560022Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2509.04298","last_updated":"2025-09-04T15:13:29Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-14T09:41:41.899897Z","submitted_at":"2025-09-04T15:13:29Z","title":"Noisy Label Refinement with Semantically Reliable Synthetic Images"},"reference_resolution":{"displayed":25,"state_counts":{"malformed_identifier":1,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":7,"verified_exact":1,"verified_fuzzy":15},"total_outbound_references":25},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 1 inbound Pith citation observation for arXiv:2509.04298."}